citation.py

import argparse
import time
import numpy as np
import networkx as nx
import torch
import torch.nn as nn
import torch.nn.functional as F
from dgl import DGLGraph
from dgl.data import register_data_args, load_data
from dgl.nn.pytorch.conv import GMMConv


class MoNet(nn.Module):
    def __init__(self,
                 g,
                 in_feats,
                 n_hidden,
                 out_feats,
                 n_layers,
                 dim,
                 n_kernels,
                 dropout):
        super(MoNet, self).__init__()
        self.g = g
        self.layers = nn.ModuleList()
        self.pseudo_proj = nn.ModuleList()

        # Input layer
        self.layers.append(
            GMMConv(in_feats, n_hidden, dim, n_kernels))
        self.pseudo_proj.append(
            nn.Sequential(nn.Linear(2, dim), nn.Tanh()))

        # Hidden layer
        for _ in range(n_layers - 1):
            self.layers.append(GMMConv(n_hidden, n_hidden, dim, n_kernels))
            self.pseudo_proj.append(
                nn.Sequential(nn.Linear(2, dim), nn.Tanh()))

        # Output layer
        self.layers.append(GMMConv(n_hidden, out_feats, dim, n_kernels))
        self.pseudo_proj.append(
            nn.Sequential(nn.Linear(2, dim), nn.Tanh()))
        self.dropout = nn.Dropout(dropout)

    def forward(self, feat, pseudo):
        h = feat
        for i in range(len(self.layers)):
            if i != 0:
                h = self.dropout(h)
            h = self.layers[i](
                self.g, h, self.pseudo_proj[i](pseudo))
        return h

def evaluate(model, features, pseudo, labels, mask):
    model.eval()
    with torch.no_grad():
        logits = model(features, pseudo)
        logits = logits[mask]
        labels = labels[mask]
        _, indices = torch.max(logits, dim=1)
        correct = torch.sum(indices == labels)
        return correct.item() * 1.0 / len(labels)

def main(args):
    # load and preprocess dataset
    data = load_data(args)
    g = data[0]
    if args.gpu < 0:
        cuda = False
    else:
        cuda = True
        g = g.to(args.gpu)
    features = g.ndata['feat']
    labels = g.ndata['label']
    train_mask = g.ndata['train_mask']
    val_mask = g.ndata['val_mask']
    test_mask = g.ndata['test_mask']
    in_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = g.number_of_edges()
    print("""----Data statistics------'
      #Edges %d
      #Classes %d
      #Train samples %d
      #Val samples %d
      #Test samples %d""" %
          (n_edges, n_classes,
           train_mask.sum().item(),
           val_mask.sum().item(),
           test_mask.sum().item()))

    # graph preprocess and calculate normalization factor
    g = g.remove_self_loop().add_self_loop()
    n_edges = g.number_of_edges()
    us, vs = g.edges(order='eid')
    udeg, vdeg = 1 / torch.sqrt(g.in_degrees(us).float()), 1 / torch.sqrt(g.in_degrees(vs).float())
    pseudo = torch.cat([udeg.unsqueeze(1), vdeg.unsqueeze(1)], dim=1)

    # create GraphSAGE model
    model = MoNet(g,
                  in_feats,
                  args.n_hidden,
                  n_classes,
                  args.n_layers,
                  args.pseudo_dim,
                  args.n_kernels,
                  args.dropout
                  )

    if cuda:
        model.cuda()
    loss_fcn = torch.nn.CrossEntropyLoss()

    # use optimizer
    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)

    # initialize graph
    dur = []
    for epoch in range(args.n_epochs):
        model.train()
        if epoch >= 3:
            t0 = time.time()
        # forward
        logits = model(features, pseudo)
        loss = loss_fcn(logits[train_mask], labels[train_mask])

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if epoch >= 3:
            dur.append(time.time() - t0)

        acc = evaluate(model, features, pseudo, labels, val_mask)
        print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
              "ETputs(KTEPS) {:.2f}".format(epoch, np.mean(dur), loss.item(),
                                            acc, n_edges / np.mean(dur) / 1000))

    print()
    acc = evaluate(model, features, pseudo, labels, test_mask)
    print("Test Accuracy {:.4f}".format(acc))


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='MoNet on citation network')
    register_data_args(parser)
    parser.add_argument("--dropout", type=float, default=0.5,
                        help="dropout probability")
    parser.add_argument("--gpu", type=int, default=-1,
                        help="gpu")
    parser.add_argument("--lr", type=float, default=1e-2,
                        help="learning rate")
    parser.add_argument("--n-epochs", type=int, default=200,
                        help="number of training epochs")
    parser.add_argument("--n-hidden", type=int, default=16,
                        help="number of hidden gcn units")
    parser.add_argument("--n-layers", type=int, default=1,
                        help="number of hidden gcn layers")
    parser.add_argument("--pseudo-dim", type=int, default=2,
                        help="Pseudo coordinate dimensions in GMMConv, 2 for cora and 3 for pubmed")
    parser.add_argument("--n-kernels", type=int, default=3,
                        help="Number of kernels in GMMConv layer")
    parser.add_argument("--weight-decay", type=float, default=5e-4,
                        help="Weight for L2 loss")
    args = parser.parse_args()
    print(args)

    main(args)